Applications of artificial intelligence in diagnosis of uncommon cystoid macular edema using optical coherence tomography imaging: A systematic review.

Cystoid macular edema (CME) is a sight-threatening condition often associated with inflammatory and diabetic diseases. Early detection is crucial to prevent irreversible vision loss. Artificial intelligence (AI) has shown promise in automating CME diagnosis through optical coherence tomography (OCT) imaging, but its utility needs critical evaluation.

Prediction of substantial closed-globe injuries in orbital wall fractures.

Purpose: To determine risk factors for substantial closed-globe injuries in orbital fractures (SCGI) and to develop the best multivariate model for the prediction of SCGI.

Methods: A retrospective study was performed on patients diagnosed with orbital fractures at Farabi Hospital between 2016 and 2022. Patients with a comprehensive ophthalmologic examination and orbital CT scan were included.

Machine learning applications for early detection of esophageal cancer: a systematic review.

Introduction: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions.

Teleburn: Designing A Telemedicine Application to Improve Burn Treatment.

Background: Due to the increasing rate of the burn injuries and a limited number of specialized treatment centers, providing medical advice and medical care at the point of need is necessary. The aim of the present study was to design and implement a teleburn system to enhance the quality of care for the burn patients.

Methods: This study was completed in 2016.